在线学习模糊回声状态网络与冗余操纵器上的应用程序
Yanqiu Li1, Huan Liu1, Hailong Gao2
1School of Data Science and Artificial Intelligence, Jilin Engineering Normal University, Changchun, China.
Frontiers in neurorobotics
|August 1, 2024
概括
本研究介绍了一种在线学习模糊回声状态网络 (OLFESN),用于解决冗余操纵器的逆动力学,提高控制效率. 实验证实了 OLFESN 的存在.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 冗余操纵器对于效率至关重要,但在反向动力学方面存在挑战.
- 控制冗余操纵器需要复杂的方法来处理它们复杂的关节配置.
研究的目的:
- 为使用先进的人工智能对冗余操纵器开发有效的控制方案.
- 为了解决冗余操纵器控制中的反向动力学问题.
主要方法:
- 提出了一个在线学习模糊回声状态网络 (OLFESN).
- OLFESN集成了一个在线学习回声状态网络与Takagi-Sugeno-Kang模糊推理系统 (FIS).
- 为冗余操纵器设计了一个基于OLFESN的控制方案.
主要成果:
- 基于OLFESN的控制方案证明了对冗余操纵器的有效控制.
- 模拟和实验验证了UR5和Franka Emika Panda机器人的拟议方法.
结论:
- OLFESN为冗余操纵器的反向动力学提供了一个可行的解决方案.
- 拟议的控制方案显著提高了冗余操纵器的效率和指令.
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